Temporal disruption in tuberculosis incidence patterns during COVID-19: a time series analysis in China.

ABSTRACT:

Background

Despite extensive knowledge of tuberculosis (TB) and its control, there remains a significant gap in understanding the comprehensive impact of the COVID-19 pandemic on TB incidence patterns. This study aims to explore the impact of COVID-19 on the pattern of pulmonary tuberculosis in China and examine the application of time series models in the analysis of these patterns, providing valuable insights for TB prevention and control.

Methods

We used pre-COVID-19 pulmonary tuberculosis (PTB) data (2007-2018) to fit SARIMA, Prophet, and LSTM models, assessing their ability to predict PTB incidence trends. These models were then applied to compare the predicted PTB incidence patterns with actual reported cases during the COVID-19 pandemic (2020-2023), using deviations b

SUBMITTER: Zhang J 

PROVIDER: S-EPMC11648691 | biostudies-literature | 2024

REPOSITORIES: biostudies-literature